Data ranking with a Lorentzian fuzzy score

ABSTRACT

The present invention relates to a method for searching a document database such as the Internet and ranking the results obtained from such a search. The invention also relates to ranking of a set of numerical data according to a set of user specified preferences, including target range, fuzziness and bias. A fuzzy score is calculated for each database record satisfying a query and the results ranked according to fuzzy score. The fuzzy score is calculated using a Lorentzian fuzzy score formula.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 09/952,518,filed Sep. 12, 2001 now U.S. Pat. No. 6,701,312. Each and everydocument, including patents and publications, cited herein isincorporated herein by reference in its entirety as though recited infull.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to information retrieval in a dataprocessing system. The present invention further relates to a method forsearching a document database such as the Internet and ranking theresults obtained from such a search.

2. Description of Related Art

A computer's logic is both its strength and its weakness; it can onlyperform what it is told to do. If an alarm clock is set to go off at6:00 PM, it will go off at exactly that time, even if it was obviouslymeant to go off at 6:00 AM. People in the real world can solve problemsand make decisions relatively easy, but even the simplest decisions areoften too difficult to be handled by computer. Fuzzy logic queryprocessing helps to bridge the gap.

Databases are strategic tools because they support business processes.In order for a database to be useful, data must be compiled intoinformation using tools such as queries. Queries allow a user to specifywhat data to retrieve from a database, and in what form. Fuzzy queryingprovides a way to retrieve data that was intended to be retrieved,without requiring exact parameters to be defined.

Non-fuzzy query processing relies on Boolean logic, which limits resultsto true or false (1 or 0). Fuzzy query processing is a superset ofBoolean logic that can handle partial truths. Instead of a searchresults being limited to return a value of true or false, the queryreturns values as x % true or x % a member of a subset.

Fuzzy queries rely on the use of fuzzy quantifiers. Dr. Lotfi A. Zadeh,the founder of fuzzy logic theory, defined two kinds of quantifiers:absolute and relative. An absolute quantifier can be represented asfuzzy subsets of the non-negative numbers and use words such as at leastthree or about five. Relative qualifiers are represented as fuzzysubsets of the unit interval and use words such as most, at least half,or almost all.

Fuzzy queries do not take the place of the more structured queries, butexpand the alternatives available. Boolean systems use selection andthen ordering as a mechanism, where a fuzzy system relies on a singlemechanism of overall membership degree. A fuzzy system allows forcompromise between the various criteria, where a Booleen system canproduce a subset of previously selected elements. There are times whenBooleen logic is too rigid to be meaningful to a user. A fuzzy queryallows a user to find elements that satisfy a criterion and ranks theresults.

FIG. 1 illustrates the typical flow of a fuzzy database query. Afteridentifying the need for a report 100, the user queries a database 110.The database returns a record, which is matched against predefinedcriteria 120 to determine the degree to which a match has occurred. Thedegree is then compared to a threshold value 125 to determine whetherthe record satisfies the users query 130 or whether the record should bediscarded 135. In general, a fuzzy database query differs from anon-fuzzy query by adding steps to match the data to predefined criteriaand compare the value to a threshold specified in the query.

An object can be a member of multiple sets with a different degree ofmembership. The degree of membership is a scale from zero to one.Complete membership has a value of one, and no membership has a value ofzero. When running a fuzzy query in a control system, the output iscalculated based on the value of membership a given input has in theconfigured fuzzy sets. Each combination of sets is configured to have aspecified output. The output is based on the weighted sum of the amountof membership in each set. The fuzzy models may be used in conjunctionwith probabilistic models to find a solution.

FIG. 2 shows the three transformations of the system inputs 200 tooutputs 205 in a fuzzy system. The process of “fuzzification” 210 is amethodology to generalize any specific theory from a precise form tocontinuous form. It decomposes a system input or output into one or morefuzzy sets. After the decomposition into fuzzy sets, fuzzy ruleassociation 215 applies a set of rules to a combination of inputs. Therules determine the action and relate the variable into a numeric value.Once the numeric value is determined, de-fuzzification 220 converts thefuzzy result into an exact output value.

For example, telling a driving student to apply the brakes 74 feet fromthe crosswalk is too precise to be followed. Vague wording like “applythe brakes soon”, however, can be interpreted and acted upon. Theinstruction is received in a fuzzy form, the person associates themessage using past experiences, then defuzzifies the message in order toactually apply the brakes at the appropriate time. Fuzzy queries expandquery capabilities by allowing for ambiguity and partial membership.

SUMMARY OF THE INVENTION

The invention relates to database searching and the ranking of a set ofnumerical data according to a set of user specified preferences,including target range, fuzziness and bias.

In many database query applications, data records are returned whencertain field data falls into a user specified target range. Theintroduction of fuzziness in the present invention extends the returneddata set by including records that are “close” to the target range. Theaddition of a bias also increases the usefulness of the database queryby providing a means to rank the results of the query in a specifiedorder.

The present invention adapts the Lorentzian function to includevariables for fuzziness and bias in order to calculate fuzzy scores,which are used to rank the results of database searches. In oneembodiment, only a single input target range is used in the databasequery. In another embodiment, however, multiple query fields are used.According to another embodiment, when multiple query fields are usedfuzzy scores are calculated for each query field in each record. Thefuzzy scores of each query field in each record are then aggregated intoa composite fuzzy score that is then used to rank the results of thedatabase query.

Other features, advantages, and embodiments of the invention are setforth in part in the description that follows, and in part, will beobvious from this description, or may be learned from the practice ofthe invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of this invention willbecome more apparent by reference to the following detailed descriptionof the invention taken in conjunction with the accompanying drawings.

FIG. 1 shows the typical flow of a fuzzy database query.

FIG. 2 depicts the three main transformations in a fuzzy system.

FIG. 3 shows a graph of the Lorentzian function.

FIG. 4 shows calculated Lorentzian fuzzy scores where the bias is lessthan zero.

FIG. 5 shows calculated Lorentzian fuzzy scores where the bias isgreater than zero.

FIG. 6 shows two graphs of calculated Lorentzian fuzzy scores where thefuzziness parameters are set to zero (i.e. there is no fuzziness).

FIG. 7 shows two graphs of calculated Lorentzian fuzzy scores in thespecial case where the target range degenerates into a single value.

FIG. 8 shows a table of calculated fuzzy scores for each record basedonly on selling price.

FIG. 9 shows a table of calculated fuzzy scores for each record basedonly on number of rooms.

FIG. 10 shows a table of calculated fuzzy scores for each record basedonly on number of baths.

FIG. 11 shows aggregate fuzzy scores for each record and ranked databaserecords.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

As embodied and broadly described herein, the preferred embodiments ofthe present invention are directed to a method for searching a documentdatabase such as the Internet according to a set of user specifiedpreferences, including target range, fuzziness and bias and ranking theresults obtained from such a search.

The basic building block of the fuzzy score is the Lorentzian function:

$\mspace{20mu}\frac{1}{1 + \lbrack \frac{x - {x0}}{a} \rbrack^{2}}$As shown in FIG. 3, the Lorentzian function 300 has a bell-shaped dropoff from a central peak. FIG. 3 depicts the Lorentzian function witha=5, x0=0, and x ranging from −10 to 10. The fuzziness of the score isproportional to the width of the Lorentzian function. The bias isintroduced by a linear variation within the target range.

The fuzzy score allows a user to rank a set of numerical data accordingto the user's input. The qualitative behavior of the Lorentzian fuzzyscore is described as follows. For data that lies outside the targetrange, the score is zero without fuzziness and in the range [0,1] withfuzziness. For data that is inside the target range, the score varieslinearly, with a bias (higher score) towards either the lower or upperbound of the target range.

The following notation and terminology is used throughout thespecification:

Name Symbols Notes Data Value x₁, x₂, . . . , x_(k) The maximal andminimal data values are Max(x_(k)) and Min(x_(k)), respectively TargetRange (x_(min), x_(max)) x_(max) ≧ x_(min) Bias β β may be greater orsmaller than zero Fuzzy Parameters $\begin{matrix}{{a1} = {{\alpha 1}*\frac{\Delta}{2}}} \\{{a2} = {{\alpha 2}*\frac{\Delta}{2}}}\end{matrix}\quad$ Δ = Max(x_(k)) − Min(x_(k)) Fuzziness or Closeness α1and α2 α1 ≧ 0 and α2 ≧ 0

According to an embodiment of the present invention, the presentinvention can calculate a fuzzy score for a user defined database queryand rank the results of the query. The fuzzy score is calculated basedon user specified criteria including, but not limited to, target range(x_(min), x_(max)), fuzziness (α1 and α2) and bias (β) In anotherembodiment, the fuzziness and/or bias is static and set by thesoftware/system performing the search. While the parameters forfuzziness and bias are numeric, it is contemplated in at least oneembodiment that these parameters be translated into more easilyunderstood terminology for user selection. For example, instead ofhaving a user specify a numeric value for each of the fuzzinessparameters, α1 and α2, the user may select from a list of fuzzinesscategories (i.e. small, medium, large). The terms small, medium andlarge would equate to specific values of α1 and α2 and would be used tocalculate a fuzzy score as described herein. The same holds true for thebias parameter, β. Instead of having a user specify a numeric biasvalue, the user may select from a list of bias categories (i.e. towardthe lower bound, toward the upper bound). These categories would also beequated to specific numeric values and would be used to calculate afuzzy score as described herein.

In order to illustrate the application of the Lorentzian function tocalculate a fuzzy score, several examples will be given. Each examplewill illustrate how variations in user input parameters (i.e. targetvalues, fuzziness, bias) affect the Lorentzian function as it is used tocalculate a fuzzy score. For consistency, the examples will be based ona user who is shopping for a new home via a web site containing new homedata. The web site allows the user to search a database of new homesbased on the selling price of the home. For the following examples it isassumed the user is interested in houses between $200,000 and $250,000.Thus, the target range is defined as x_(min)=200000 and x_(max)=250000.In addition, the web site allows the user to input fuzziness values, α1and α2, and a bias value, β.

For bias (β) values less than zero (i.e. biased toward the lower boundof the target range), the Lorentzian fuzzy score has the followingformula:For β<0,

${S(x)} = \begin{matrix}\frac{1}{1 + ( \frac{x - x_{\min}}{a1} )^{2}} & {x < x_{\min}} \\{1 + \frac{\beta*( {x - x_{\min}} )}{x_{\max} - x_{\min}}} & {x_{\min} \leq x \leq x_{\max}} \\\frac{1 + \beta}{1 + ( \frac{x - x_{\max}}{a2} )^{2}} & {x_{\max} < x}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

FIG. 4 shows a sample plot 400 of S(x) with x_(min)=200000,x_(max)=250000, α1=2, α2=1 and β=−0.1. The negative slope of the graphbetween the target values $200,000 and $250,000 is the result of thenegative bias. The negative bias affects the fuzzy score calculated fromdata values between the target range by biasing those data values closerto the lower end of the target range. In addition, worth noting are thecalculated fuzzy scores for the data points that lie outside the targetrange. The non-zero scores for those data points lying outside thetarget range are a direct result of the incorporation of the fuzzinessparameters into the Lorentzian function. Consistent with the Lorentzianfunction is the rapid drop off of the fuzzy scores for the data pointsthat lie farthest from the target range. In this example, and asillustrated in the table of result rankings 410, the query would havereturned records for each of the 12 homes in the database. The additionof the fuzzy scores, however, makes it easy for the user to visualizethe records that best conform to the original search parameters.

For bias (β) values greater than zero (i.e. biased toward the upperbound of the target range), the Lorentzian fuzzy score has the followingformula:For β>0,

${S(x)} = \begin{matrix}\frac{1 - \beta}{1 + ( \frac{x - x_{\min}}{a1} )^{2}} & {x < x_{\min}} \\{1 + \frac{\beta*( {x - x_{\max}} )}{x_{\max} - x_{\min}}} & {x_{\min} \leq x \leq x_{\max}} \\\frac{1}{1 + ( \frac{x - x_{\max}}{a2} )^{2}} & {x_{\max} < x}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

FIG. 5 shows a sample plot 500 of S(x) with x_(min)=200000,x_(max)=250000, α1=2, α2=1, and β=0.1. The positive slope of the graphbetween the target values $200,000 and $250,000 is the result of thepositive bias. The positive bias affects the fuzzy score calculated fromdata values between the target range by biasing those data values closerto the upper end of the target range. In addition, worth noting are thecalculated fuzzy scores for the data points that lie outside the targetrange. The non-zero scores for those data points lying outside thetarget range are a direct result of the incorporation of the fuzzinessparameters into the Lorentzian function. Consistent with the Lorentzianfunction is the rapid drop off of the fuzzy scores for the data pointsthat lie farthest from the target range. In this example, and asillustrated in the table of result rankings 510, the query would havereturned records for each of the 12 homes in the database. The additionof the fuzzy scores, however, makes it easy for the user to visualizethe records that best conform to the original search parameters.

In the case of no fuzziness, α1=0, α2=0, and where the bias (β) value isless than zero (i.e. biased toward the lower bound of the target range),the Lorentzian fuzzy score has the following formula:For β<0, α1=0, and α2=0,

${S(x)} = \begin{matrix}0 & {x < x_{\min}} \\{1 + \frac{\beta*( {x - x_{\min}} )}{x_{\max} - x_{\min}}} & {x_{\min} \leq x \leq x_{\max}} \\0 & {x_{\max} < x}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

In the case of no fuzziness, α1=0, α2=0, and where the bias (β) value isgreater than zero (i.e. biased toward the upper bound of the targetrange), the Lorentzian fuzzy score has the following formula:For β>0, α1=0, and α2=0,

${S(x)} = \begin{matrix}0 & {x < x_{\min}} \\{1 + \frac{\beta*( {x - x_{\max}} )}{x_{\max} - x_{\min}}} & {x_{\min} \leq x \leq x_{\max}} \\0 & {x_{\max} < x}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

FIG. 6 shows a sample plot 600 of S(x) with x_(min)=200000,x_(max)=250000, α1=0, α2=0, and β=−0.1. FIG. 6 also shows another sampleplot 610 of S(x) with x_(min)=200000, x_(max)=250000, α1=0, α2=0, andβ=0.1. As with plots 400 and 500,plots 600 and 610 illustrate the effecta negative and positive bias have on the calculated fuzzy scores. Aspreviously stated, a negative bias results in the negative slope of theplot 600 between the target values, while a positive bias results in thepositive slope of the plot 610 between the target values. Since thefuzziness parameters, α1 and α2, were set to zero in both plot 600 andplot 610, all data values lying outside of the user defined target rangeare given a fuzzy score of zero. While the same query produced 12records with fuzziness (as illustrated in FIGS. 4 and 5), withoutfuzziness only 4 records are retrieved. Moreover, records that wouldprobably be of interest to the user, i.e. the $252,000 home, are neverretrieved when the query does not incorporate the fuzziness parameters.

In the special case where the target range degenerates into a singlevalue (i.e. x_(min)=x_(max)=x) and where the bias (β) value is less thanzero (i.e. biased toward the lower bound of the target range), theLorentzian fuzzy score has the following formula:For β<0 and x_(min)=x_(max)=x,

${S(x)} = \begin{matrix}\underset{\_}{\frac{1}{1 + ( \frac{x - \underset{\_}{x}}{a1} )^{2}}} & {x < \underset{\_}{x}} \\1 & {x = \underset{\_}{x}} \\\frac{1 + \beta}{1 + ( \frac{x - \underset{\_}{x}}{a2} )^{2}} & {x > \underset{\_}{x}}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

In the special case where the target range degenerates into a singlevalue (i.e. x_(min)=x_(max)=x) and where the bias (β) value is greaterthan zero (i.e. biased toward the upper bound of the target range), theLorentzian fuzzy score has the following formula:For β>0 and x_(min)=x_(max)=x,

${S(x)} = \begin{matrix}\underset{\_}{\frac{1 - \beta}{1 + ( \frac{x - \underset{\_}{x}}{a1} )^{2}}} & {x < \underset{\_}{x}} \\1 & {x = \underset{\_}{x}} \\\frac{1}{1 + ( \frac{x - \underset{\_}{x}}{a2} )^{2}} & {x > \underset{\_}{x}}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)}.

FIG. 7 shows a sample plot 700 of S(x) with x=225000, α1=1, α2=1, andβ=−0.1. FIG. 7 also shows another sample plot 710 of S(x) with x=225000,α1=1, α2=1, and β=0.1. The affect of the bias in plot 700 and 710 ismore difficult to discern since there is no target range. However, aclose inspection of plot 700 shows that the negative bias does influencethe calculated fuzzy scores for those data values less than the targetvalue and in plot 710 that the positive bias does influence thecalculated fuzzy scores for those data values greater than then targetvalue.

While each of the above examples has dealt with single term queries(i.e. where the user is searching based only on the selling price, of ahome), the present invention is easily extended to include queries withmultiple search terms. To illustrate how the Lorentzian function can beused in a multiple-field query a more sophisticated home buying exampleis explored. The home buying database in this example is on the Internetand the user is given the option to search for new homes based onselling price, number of rooms, and number of baths. For this example,it is assumed the database contains the following records:

Selling Price $150,000 $170,000 $195,000 $200,000 $225,000 $230,000$235,000 $260,000 $280,000 Number 3 2 3 3 5 3 4 4 3 of Rooms Number 2.52 4 3 3.5 2 3 3 1.5 of BathsIt is further assumed that the user has entered the following query:

Selling Price: $200,000–$230,000 Bias: Lower Priced Homes Number ofRooms: 3 Bias: More Rooms Number of Baths: 3 Bias: More Baths Fuzziness:Medium

As previously discussed, and as illustrated here, the database or searchtool/engine may be set up to allow the user to input non-numeric biasand fuzziness then equated to specific numeric parameters by thedatabase application, search tool/engine or other system/software. Forthis example, the following non-numeric user specified bias parametersare equated to the following numeric parameters: Bias Lower PricedHomes=−0.1, More Rooms=0.1, More Baths=0.1. In addition, for thisexample, the following non-numeric specified fuzziness parameter isequated to the following numeric parameters: Medium→α1=2, α2=2. Whilethe user, in this example, was only allowed to enter a single fuzzinessparameter for the entire query, it is contemplated in another embodimentthat each query field (i.e. selling price, number of rooms, number ofbaths) could have its own separate fuzziness parameter.

The process for searching the database and ranking the results of thequery is straightforward. First, a fuzzy score is calculated for eachquery field and for each record using the appropriate Lorentzianformula. Second, an aggregate fuzzy score is calculated for each recordusing the fuzzy scores for each query field. Finally, the results areranked according to the aggregate calculated fuzzy scores for eachrecord.

FIG. 8 shows the fuzzy score 800 for each record based only on sellingprice. Since the user has specified a bias toward the lower bound of thetarget range (as indicated by the user's desire for lower priced homes),the following Lorentzian fuzzy score formula is used:For β<0,

${S(x)} = \begin{matrix}\frac{1}{1 + ( \frac{x - x_{\min}}{a1} )^{2}} & {x < x_{\min}} \\{1 + \frac{\beta*( {x - x_{\min}} )}{x_{\max} - x_{\min}}} & {x_{\min} \leq x \leq x_{\max}} \\\frac{1 + \beta}{1 + ( \frac{x - x_{\max}}{a2} )^{2}} & {x_{\max} < x}\end{matrix}$where x represents any data values, i.e., x∈{x₁, . . . , x_(k)},x_(min=)200000, x_(max)=230000, α1=2, α2=2, and β=−0.1.

FIG. 9 shows the fuzzy score 900 for each record based only on number ofrooms. Since the user has specified a bias toward the upper bound of thetarget range (as indicated by the user's desire for more rooms), thefollowing Lorentzian fuzzy score formula is used:For β>0 and x_(min)=x_(max)=x,

${S(x)} = \begin{matrix}\underset{\_}{\frac{1 - \beta}{1 + ( \frac{x - \underset{\_}{x}}{a1} )^{2}}} & {x < \underset{\_}{x}} \\1 & {x = \underset{\_}{x}} \\\frac{1}{1 + ( \frac{x - \underset{\_}{x}}{a2} )^{2}} & {x > \underset{\_}{x}}\end{matrix}$where x represents any data values, i.e., xδ∈{x₁, . . . , x_(k)}, x=3,α1=2, α2=2, and β=0.1.

FIG. 10 shows the fuzzy score 1000 for each record based only on numberof baths. Since the user has specified a bias toward the upper bound ofthe target range (as indicated by the user's desire for more baths), andsince a single target value is also specified, the same Lorentzian fuzzyscore formula used to calculate the fuzzy scores for each record basedon number of rooms is used.

Once a fuzzy score is calculated for each query field for each record,an aggreagate fuzzy score is calculated for each record. In oneembodiment, this is accomplished by simply adding the fuzzy scores ofeach query field together. In another embodiment, an aggregate fuzzyscore is calculated by using a weighted sum. By using a weighted sum theresults of a specific query field(s) can be given more weight. Forexample, a user may consider the selling price of a home more importantthan any of the other query fields. In order to incorporate this intothe ranking methodology, the fuzzy scores calculated based only on theselling price of the home are multiplied by some factor so that theselling price of the home has more influence on the aggregate fuzzyscores.

The aggregate fuzzy scores 1100 for each record in this example areshown in FIG. 11. In this example the fuzzy scores of each query fieldhave simply been added together. Once the aggregate fuzzy scores arecalculated, the database records are ranked according to aggregate fuzzyscore. The ranked database records 1110 are also shown in FIG. 11. Withthe homes ranked, the user can easily identify those homes that bestmatch the user's query.

As would be expected based on the input query, the home selling for$200,000 with 3 rooms and 3 baths is ranked the highest. While atraditional database search would not have retrieved any records outsidethe user's input target range of $200,000-$230,000, 3 rooms and 3 baths,the present invention has returned all 9 records. Worth noting is thehome selling for $195,000 with 3 rooms and 4 baths and the home sellingfor $235,000 with 4 rooms and 3 baths. Both of these homes would nothave been included within the search results in a traditional databasesearch, but in the present invention are ranked high due to theincorporation of the fuzziness parameters in the Lorentzian fuzzy scoreformulas.

Other embodiments and uses of the present invention will be apparent tothose skilled in the art from consideration of this application andpractice of the invention disclosed herein. The present description andexamples should be considered exemplary only, with the true scope andspirit of the invention being indicated by the following claims. As willbe understood by those of ordinary skill in the art, variations andmodifications of each of the disclosed embodiments, includingcombinations thereof, can be made within the scope of this invention asdefined by the following claims.

1. A method for ranking a plurality of elements, wherein a numericalparameter characterizes each element, the method comprising: ranking aplurality of elements based at least in part on a Lorenizian fuzzy scorebased at least in part on a numerical parameter characterizing eachelement.
 2. The method of claim 1, further comprising, prior to ranking:receiving an indication of at least one of fuzziness and bias as inputsto ranking.
 3. The method of claim 2 wherein at least one indication isqualitative.
 4. The method of claim 3 wherein each qualitativeindication is mapped to a quantitative indication.
 5. The method ofclaim 2 wherein at least one indication is quantitative.
 6. A method forranking a plurality of elements, wherein a plurality of numericalparameters characterizes each element, the method comprising: ranking aplurality of elements based at least in part on an aggregate score; theaggregate score based at least in part on a plurality of Lorentzianfuzzy scores; each Lorentzian fuzzy score based at least in part on anumerical parameter characterizing each element.
 7. The method of claim6, further comprising, prior to ranking: receiving an indication of atleast one of fuzziness and bias as inputs to ranking.
 8. The method ofclaim 7 wherein at least one indication is qualitative.
 9. The method ofclaim 8 wherein each qualitative indication is mapped to a quantitativeindication.
 10. The method of claim 7 wherein at least one indication isquantitative.
 11. The method of claim 6 wherein the aggregate score isbasal at least in part on summing the Lorentzian fuzzy scores.
 12. Themethod of claim 6 wherein the aggregate score is based at least in parton weighted summing of the Lorentzian fuzzy scores.
 13. The method ofclaim 12 further comprising: prior to ranking, receiving an indicationof weight for at least one parameter.